Parking enforcement may not be popular, but, when done fairly, it becomes a critical component of good transportation policy. Illegal parkers, after all, take up scarce curbside parking spaces - creating dangerous conditions, reducing turnover, and exacerbating congestion. Proper management of parking enforcement officers (“PEOs”) can help improve travel times, increase access, and make conditions safer for pedestrians and bicyclists.
But where should a municipality start? We propose four steps to improving parking enforcement.
Step One: Performance Management
Parking enforcement is a measurable activity with measurable outcomes, and cities owe it to their constituencies to analyze the data. Issuing lots of citations doesn't necessarily help improve the customer experience, so parking managers should review the quality of citation issuance as well. Towards that end, we provide cities with dashboards that help managers understand:
- Are enforcement zones being enforced equitably?
- Are citations accurate? Erroneous and poorly written citations cause motorists unnecessary pain and waste time and resources.
- Are there too many lengthy gaps between citations? We can study latent time—or periods of inactivity—as well as patrol periods to improve productivity.
- Is parking enforcement changing behavior?
- Are supervisors properly monitoring their teams?
- Are safety violations being prioritized properly?
By studying data, we can gain real insight into the effectiveness of PEOs and parking programs. This mix of qualitative and quantitative metrics can be used in turn to reward performance and train personnel.
Step Two: Shift Improvement
Another way cities can improve performance is to align staff schedules with the likelihood of finding citations. Optimizing the start and end times of PEO shifts helps ensure appropriate coverage, improve citation issuance and capture rates, and reduce downtime. Too often, too few or too many personnel are scheduled to enforce during a given hour. By comparing the distribution of staff to the likelihood of citations per hour, we can determine the probability of citations across a day. Analytics can help by:
- Optimizing the number of PEOs distributed across various shift start and end times
- Increasing the ratio of supervisors to PEOs
- Improving the correlation of enforcement hours to predicted citations
- Improving traffic flow by ensuring adequate enforcement during rush hour
- Increasing productivity
Step Three: New Enforcement Zones
A lot of guesswork goes into creating enforcement boundaries, or "beats." While that guesswork may be based on ideas about meter payments, land use, business districts, and population distribution, it's often based on assumptions. Sometimes these beats aren’t modified for years, despite changing demand and use. Cities, can use data to improve the size and shape of their enforcement zones. Realigning beats can reduce predatory issuance, or the potential for PEOs to focus too much on a neighborhood or even particular vehicles.
We use algorithms to revise enforcement zone maps, nudging boundaries to best align enforcement with the need for turnover, increasing productivity and reducing time to enforce. These revised boundaries then serve to communicate further information to PEOs, including mobile access to hour-by-hour regulatory maps, citation probabilities, and predictive enforcement routes.
Step Four: Probability Mapping and Routing
Using historical data like citation issuance trends and meter payments, data proxies, and predictive algorithms, we can forecast the likelihood of citations within PEO zones. We provide PEOs with maps to better understand where and when illegal parking is most likely to occur within an enforcement zone.
While understanding the likelihood of finding citations is great, that data may not provide insights into how a PEO can best work an area to optimize time and productivity over a longer period of time. Consequently, we prioritize enforcement routes within these zones for PEOs. These routes are available to PEOs with the push of a button and can help supplement the PEO enforcement toolbox, helping them to verify assumptions about optimization or change up their routine.
Armed with advanced degrees and training in computer science, engineering, math and statistics, economic theory, computer vision, and machine learning, our big data “wranglers” provide proactive solutions to improve productivity for our municipal clients. We provide cities with the tools for sound decision-making and justification for their enforcement decisions, helping cities to increase parking turnover, improve safety, and mitigate congestion.
About the AuthorMore Content by Matt Darst